Course materials for ZTF Summer School 2021

This course will introduce key concepts and techniques used to work with large datasets, in the context of the field of astrophysics. In the first 3 days of the course, the focus will be on the modern approaches to creating and manipulating large data sets, with the focus on time series analyses and Bayesian methods applied to astrophysics survey data. The remaining part of the course will focus on a range of machine learning techniques for processing data: classification algorithms (supervised and unsupervised learning), clustering algorithms, regression problems, recommender systems, graphic models and others. The algorithms will first be introduced in lectures, and the emphasis will then be placed on homework worked as teams in which the students will apply the algorithms (and already available packages) to astrophysical data sets to answer specific astrophysics questions. The course will assume familiarity with basic concepts in astrophysics, but it will include brief reviews as needed to demonstrate the use of modern data analysis techniques.

Day 1:

• ZTF Introduction
• Microlensing
• Light curve access

Day 2:

• Cataclysmic Variables
• Alert stream access

Day 3:

• Period Finding / Variability Metrics
• White Dwarf Binaries

Day 4:

• Introduction to Machine Learning
• ZTF SCoPe

Day 5:

• Deep Learning
• ZTF SCoPe (continued)

• Fork this repo

• The topics of each day's lectures are described in the schools' program

• Lectures are in directories named XY/ where XY/ is the number of the day

• Homework assignments are similarly in the homework/ directory

• Solutions to homework assignments will be posted the day after they are due.

• Various help cheat sheets are included in help/.

• You should also review in-class notebooks and homework solutions to make sure you understand what is happening

• The lecture notebooks have in-class exercises for you to work on

• UIUC Fundamentals of Data science: https://github.com/gnarayan/ast596_2020_Spring
• Caltech Astroinformatics: https://sites.astro.caltech.edu/ay119/
• GROWTH summer school: http://growth.caltech.edu/growth-school-2019.html
• AURA winter school: http://www.aura-o.aura-astronomy.org/winter_school/ - go to Past Years.
• YouTube Neural Networks: https://www.youtube.com/watch?v=aircAruvnKk
• UMN Big Data Astronomy: https://github.com/mcoughlin/ast8581_2021_Spring

If you are new to github, we recommend watching:

• https://www.youtube.com/watch?v=USjZcfj8yxE&ab_channel=ColtSteele
• https://www.youtube.com/watch?v=nhNq2kIvi9s&ab_channel=ColtSteele

If running locally, we suggest using anaconda: https://anaconda.org/

If you are on windows, use the windows subsystem for linux: https://docs.microsoft.com/en-us/windows/wsl/install-win10 plus the ubuntu anaconda.

After installing anaconda, you should (in a terminal):

• clone the repository: git clone [email protected]:mcoughlin/ztf_summer_school_2021.git
• change directories to the repository: cd ztf_summer_school_2021
• use conda to create a repository
  • on mac osx: conda env create -f environment-osx.yml
  • on linux / windows wsl: conda env create -f environment-ubuntu.yml

Activate the environment with:

• source activate ztfsummer and can test by opening the first lecture:
• cd lectures/01
• jupyter notebook lecture01.ipynb

Either will, you will need to pip install:

• pip install git+git://github.com/colcarroll/minimc.git

Otherwise, google colaboratory will work: https://colab.research.google.com/

Will need to create an IRSA account for data access: https://irsa.ipac.caltech.edu/frontpage/